Line Plots in R-Time Series Data Visualization {https://t.co/NDxNGa4b5S} #rstats #DataScience
— R-bloggers (@Rbloggers) October 10, 2021
A gentle introduction to dynamical systems theory {https://t.co/zxAbgRljOT} #rstats #DataScience
— R-bloggers (@Rbloggers) October 8, 2021
Visualizing trees with Sklearn {https://t.co/wLSuJ5KLEx} #rstats #DataScience
— R-bloggers (@Rbloggers) October 6, 2021
A Bayesian analysis of a factorial design focusing on effect size estimates {https://t.co/cfVjK5PEoi} #rstats #DataScience
— R-bloggers (@Rbloggers) October 12, 2021
Infectious diseases and nonlinear differential equations {https://t.co/NLMlEiQS1W} #rstats #DataScience
— R-bloggers (@Rbloggers) October 11, 2021
Causal effect of Elon Musk tweets on Dogecoin price {https://t.co/HsS0L1g30t} #rstats #DataScience
— R-bloggers (@Rbloggers) October 8, 2021
Estimating the risks of partying during a pandemic {https://t.co/pFoCAUOnlQ} #rstats #DataScience
— R-bloggers (@Rbloggers) October 10, 2021
Data Visualization with R-Scatter plots {https://t.co/4IcqPYfhOa} #rstats #DataScience
— R-bloggers (@Rbloggers) October 9, 2021
Data Visualization with R-Scatter plots {https://t.co/uvnZlbfreM} #rstats #DataScience
— R-bloggers (@Rbloggers) October 8, 2021
Black-Derman-Toy Interest Rate model using R {https://t.co/2eHNPuD5kC} #rstats #DataScience
— R-bloggers (@Rbloggers) October 9, 2021
How to Make a Heatmap of Customers in R [Video] {https://t.co/q4u5K9BmYX} #rstats #DataScience
— R-bloggers (@Rbloggers) October 12, 2021
Introduction to Recurrent Neural Networks {https://t.co/VlXvAcHEfC} #rstats #DataScience
— R-bloggers (@Rbloggers) October 10, 2021
Machine Learning with R: A Complete Guide to Decision Trees {https://t.co/sWNj1Zsbpk} #rstats #DataScience
— R-bloggers (@Rbloggers) October 2, 2021
Machine Learning with R: A Complete Guide to Logistic Regression {https://t.co/UR3YCTg346} #rstats #DataScience
— R-bloggers (@Rbloggers) October 3, 2021
How to Analyze Data with R: A Complete Beginner Guide to dplyr {https://t.co/EmyvyYs6pv} #rstats #DataScience
— R-bloggers (@Rbloggers) October 3, 2021
Advances in Difference-in-Differences in Econometrics {https://t.co/AZrL5Ctsb6} #rstats #DataScience
— R-bloggers (@Rbloggers) September 30, 2021
Download recently published book – Learn Data Science with R {https://t.co/SQt1rmWZAL} #rstats #DataScience
— R-bloggers (@Rbloggers) September 14, 2021
Machine Learning with R: A Complete Guide to Linear Regression {https://t.co/SQlezk20rc} #rstats #DataScience
— R-bloggers (@Rbloggers) September 27, 2021
How to Remove Outliers in R {https://t.co/IcHI3c7m1U} #rstats #DataScience
— R-bloggers (@Rbloggers) September 29, 2021
A Step-By-Step Guide To Web Scraping With R {https://t.co/hSAl6ImAp2} #rstats #DataScience
— R-bloggers (@Rbloggers) September 25, 2021
Machine Learning : Workflow {https://t.co/Ww5FmDMSaA} #rstats #DataScience
— R-bloggers (@Rbloggers) September 19, 2021
Top 10 dplyr Functions — Data Analysis Made Easy {https://t.co/kbrIxGHqLM} #rstats #DataScience
— R-bloggers (@Rbloggers) September 25, 2021
Animating Network Evolutions with gganimate {https://t.co/PnT0ItNPO2} #rstats #DataScience
— R-bloggers (@Rbloggers) September 16, 2021
Introduction to Deep Learning {https://t.co/Y8ITA0u9IR} #rstats #DataScience
— R-bloggers (@Rbloggers) October 1, 2021
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```